Brand Voice Guidelines for AI-Generated Content

By Camellia · Updated Oct 10, 2026

Brand voice guidelines for AI-generated content are a written, testable description of how your company sounds, built so that both a language model and a human reviewer can apply it. A model has no voice of its own, only an average of everything it has read. Without instructions it produces competent, generic copy. With a well-built guideline (attributes, examples, banned phrases and a review routine) it produces drafts that need an edit instead of a rewrite.

Most existing brand books don't do this job. They say "friendly, bold, human" and stop. A person can interpret that. A model can't, and neither can three different reviewers. The fix isn't more adjectives. It's turning each attribute into a pair of behaviors: what to do, what to avoid, with a real sentence showing each.

This article covers how to define voice attributes, write a template a model can use, build a banned-phrase list, set review steps, and keep the whole thing maintained. It's written for brand, content and marketing managers who already have some AI in their workflow. For the wider picture, see AI in marketing.

Key Facts: Brand Voice for AI

• Nielsen Norman Group describes tone of voice along four dimensions: formal vs. casual, serious vs. funny, respectful vs. irreverent, and matter-of-fact vs. enthusiastic (NN/g, Kate Moran).
• NN/g's advice is to keep brand personality consistent while varying tone to fit the user's emotional state and the topic (NN/g).
• Mailchimp's content style guide defines voice as the consistent personality of the brand and tone as how that voice adjusts to the situation (Mailchimp Content Style Guide).
• GOV.UK states that plain English is mandatory for all of GOV.UK and publishes a list of words to avoid, each with a suggested replacement (GOV.UK A to Z style guide).
• Google's guidance on generative AI content says it is critical to manually fact-check and review all AI-generated content for accuracy and trustworthiness before publishing (Google Search Central).

Voice, tone and style: three different things

Teams mix these up, and the confusion leaks straight into prompts.

• Voice is the stable personality. It's who you are on every channel and in every situation.
• Tone is how that voice flexes. A product-launch announcement and an outage apology shouldn't sound the same.
• Style is the mechanical rulebook: capitalization, number formats, spelling variants, how you write dates.

Mailchimp's style guide states the split plainly: voice is the brand's consistent personality, and tone is how it adjusts to the situation. NN/g gives the same advice from a usability angle: keep brand personality consistent, but vary the tone to fit the user's emotional state and the topic.

For AI work, this means your guideline needs at least two layers. A fixed voice layer goes into every prompt. A tone layer changes by content type and audience situation. If you collapse them into one paragraph, the model will apply the same energy to a refund email and a conference teaser.

Start with dimensions, then name your attributes

A blank page produces vague attributes. Use a scale instead. NN/g describes four dimensions of tone of voice, each with two poles:

1. Formal vs. casual
2. Serious vs. funny
3. Respectful vs. irreverent
4. Matter-of-fact vs. enthusiastic

Place your brand on each scale, and then place your tone for specific situations. A B2B payroll company might sit at "somewhat formal, serious, respectful, matter-of-fact" for its guides, then shift toward "casual, lighter, enthusiastic" for event invitations. Writing the positions down turns a feeling into a decision someone can check a draft against.

Then convert the positions into three to five named attributes. Mailchimp's own voice is a useful public example of how concrete this can be. It describes itself as plainspoken, genuine, translators of complicated marketing terms, and dry-humored, and attaches behaviors to each: clarity first, no hype or fluffy metaphors, humor only when it fits the moment. Notice that every attribute implies something you could catch in a draft.

Good attributes pass two tests:

• They can be violated. "Professional" can't be caught breaking. "Plainspoken: no word a ten-year-old would need to look up unless it's a product term" can.
• They distinguish you. If a competitor could paste the same list into their own guide, it isn't doing any work.

The template: one row per attribute

Here's the structure that works best for prompts and reviews. Each attribute gets a meaning, a do, a don't and an example pair.

Three habits make this table work for AI:

• Always include a paired example. Models imitate examples more reliably than they follow abstract rules. Give two or three "on-brand" and one or two "off-brand" samples per attribute.
• Write the don't as precisely as the do. The failure mode of generic AI copy is specific and recognizable: inflated openers, filler transitions, tidy three-part lists. Name those patterns directly.
• Keep it short enough to paste. If the guideline runs forty pages, you'll never put it in a prompt. Maintain a long reference version and a one-page "prompt version" of the same rules. For prompt mechanics, see prompt engineering.

Add a tone map by situation

The voice stays fixed. The tone dial moves with context. A small second table covers this.

This is the part NN/g's advice maps onto most directly: vary tone to fit the user's emotional state and the topic. Marketers often skip it because it feels obvious. A model won't infer it.

Examples by marketing function

Voice rules show up differently depending on what's being written. A few illustrations of how a team might apply the same template.

• Email and lifecycle. The guideline specifies subject-line length habits, how to greet, and which tone applies to onboarding versus win-back. A reviewer checks that no email opens with an inflated claim.
• Blog and SEO content. The guideline names the reading level, the stance on jargon and the structure of an answer-first opening. See AI content strategy for how voice fits a wider editorial plan.
• Paid and social ads. Short formats leave no room for nuance, so the banned list matters more. Claims need the tightest rules because ads draw the most scrutiny.
• Sales enablement. One-pagers and outreach drafts use the direct, grounded attributes, with named proof points the model is allowed to cite and nothing else.
• Support and help content. Plain language dominates. Humor is usually off. The tone map is strict about apologies and next steps.
• Product copy and UI text. Microcopy follows style rules (capitalization, button verbs) more than personality rules, so the style layer carries more weight here.

The same guideline file can feed all of these, with a different tone row selected each time. For how these uses map to the customer journey, see AI marketing use cases by funnel stage.

Build a banned and preferred phrase list

A phrase list is the cheapest control you can add, and the easiest for reviewers to apply. It has three parts.

1. Generic AI tells. Every team ends up with its own list, but common categories include inflated openers ("In today's fast-paced world"), empty intensifiers ("truly", "incredibly"), filler transitions at the start of every paragraph, and abstract verbs that sound busy and say little. Collect real examples from your own drafts rather than copying someone else's list.

2. Brand-specific bans. Words your brand never uses: competitor-flavored jargon, terms legal has rejected, nicknames for customers that you find patronizing, and any claim you can't substantiate.

3. Preferred replacements. A ban with no alternative leaves the model guessing. GOV.UK's style guide is a good public model for this format. It declares that plain English is mandatory and then lists words to avoid, each paired with a replacement: "utilise" becomes "use", "streamline" becomes "simplify", "going forward" becomes "from now on" or "in the future", and "in order to" is usually just cut. The same page also advises avoiding metaphors because they slow comprehension, and notes that technical terms are fine where you need them, because they aren't jargon.

Copy that structure: avoid, use instead, and any exception. Keep the list under about fifty items, or nobody will remember it.

Claims need their own rule

Voice guidance usually covers how you say things. For AI output you also need a rule for what you're allowed to say. Models produce confident statements about statistics, product capabilities and competitors whether or not any of it is true. Google's guidance puts it directly: generative models predict a likely sequence of words rather than retrieving facts, so outputs can be inaccurate.

Add a "claims" section to the guideline:

• Statistics must come with a named source and link, supplied by a human, not generated by the model.
• Product claims are limited to an approved fact sheet that you paste into the prompt.
• Comparisons with competitors need a human sign-off.
• Customer quotes and case studies are never invented. If the model is asked for one, it should leave a placeholder.

The related risk of invented facts is covered in AI hallucination.

How to roll out brand voice for AI: step by step

1. Audit your best existing content. Pick ten to twenty pieces that sound most like you. Highlight what they have in common: sentence length, opening moves, how they handle numbers, what they never say.
2. Place the brand on the four tone dimensions. Use the NN/g scales. Get two or three stakeholders to mark them independently, then compare. Disagreements show where the voice is still unclear.
3. Write three to five attributes in the template. One row each, with meaning, do, don't, and a paired example drawn from your real content.
4. Add the tone map. List your five or six main content situations and set the dial for each.
5. Build the phrase list. Start with the generic AI tells you see in your own drafts, add brand bans, and give a replacement for each.
6. Create the one-page prompt version. Reduce the guideline to something that fits at the top of a prompt or a saved instruction, plus two or three full example paragraphs.
7. Test with a fixed set of briefs. Run five to ten standard briefs through the model with and without the guideline. Have people who didn't write the guide rate which draft sounds like you, blind.
8. Revise from failures. Each miss usually points to a missing example or an ambiguous attribute. Fix the guideline, not just the draft.
9. Set the review routine (below) and train editors on it.
10. Assign an owner and a review date. Voice drifts as products and audiences change.

The review routine: what a human checks every time

A guideline only works if someone verifies the output against it. A short, repeatable checklist keeps reviews consistent between editors.

1. Facts. Every number, name, date and product claim traced to a source or the approved fact sheet. Google's advice is explicit that AI-generated content should be manually fact-checked and reviewed before publishing, and that includes titles, meta descriptions and image alt text.
2. Voice. Read the first paragraph aloud. Does it match the attribute table? Check each attribute once.
3. Tone. Does the setting match the situation row?
4. Banned phrases. Search for the list. Cut or replace.
5. Original value. Does the piece say anything specific that only your company could say? Google warns that generating many pages with AI without adding value for users may violate its spam policy on scaled content abuse, so volume with no added substance is a risk beyond bad style.
6. Disclosure and approvals. Does this content type need legal, compliance or a subject expert before it ships?

This is a form of human-in-the-loop control. Decide up front which content types get light review (internal summaries, first-draft ideas) and which require full review (anything public, anything with claims). The role split is covered in AI marketing team structure.

Putting the guideline into your tools

Where the guideline lives determines whether people use it.

• Saved instructions or custom assistants. Paste the one-page version into whatever persistent instruction field your AI tool offers, so every draft starts with it.
• Prompt templates. Store tone-map rows as swappable blocks: voice (fixed), tone (select), task (variable), facts (pasted).
• Editing checklists. Put the review routine in your content workflow tool as a required step, not a wiki page.
• Few-shot examples. Keep a small library of approved pieces to paste as examples. Update it when you publish something that sets a new standard.

For a view of where this sits among your other systems, see the AI marketing stack, and treat the guideline as a governed asset like any other input to the system.

Common pitfalls

• Adjectives instead of behaviors. "Authentic, bold, human" can't be checked. Rewrite each as something a reviewer can find in a draft.
• One tone for everything. The voice should hold steady and the tone should move. Skipping the tone map is the fastest route to an upbeat message in the middle of an apology.
• Examples missing. Rules without samples produce drift. Include on-brand and off-brand pairs.
• Writing the guide for humans only. If a model can't use it in a prompt, it won't change your AI output.
• A banned list with no replacements. The model swaps one filler word for another.
• Ignoring factual control. Perfect voice on an invented statistic is still a published error.
• No owner. Voice guidelines go stale the first time the product or audience shifts.
• Over-polishing. Heavy rules can flatten the writing into something stiff. Test with real readers and keep room for a human editor to add personality.